Phenomena: Stable and general features of the world in need of explanation. Can be understood as robust generalizations of patterns in empirical data. They are the explanatory targets for scientific theories (the explanandum). In psychology often called “effects”.

Data: Relatively direct observations. Refer to particular empirical patterns in concrete data sets rather than empirical generalizations (which would be phenomenona).

Theories: Something that explains phenomena of interest (the explanans). But what is a theory?

(Note: While these features of a scholarly article do not constitute a theory, they might be important in their own right)

1. References Are Not Theory

A manuscript that Robert Sutton edited had strong data, but all three reviewers emphasized that it had “weak theory” and “poorly motivated hypotheses.” The author responded to these concerns by writing a new introduction that added citations to many papers containing theory and many terms like “psycho-social theory,” “identity theory,” and “social comparison theory.”

But it still contained no discussion of what these theories were about and no discussion of the logical arguments why these theories led to the author’s predictions. The result was that this paper contained almost no theory, despite the author’s assertion that much had been added.

Solution:

Authors need to explicate which concepts and causal arguments are adopted from cited sources and how they are linked to the theory being developed or tested.

2. Data Are Not Theory

Empirical evidence plays an important role in confirming, revising, or discrediting existing theory and in guiding the development of new theory. But observed patterns like beta weights, factor loadings, or consistent statements by informants rarely constitute causal explanations.

Kaplan (1964) asserted that theory and data each play a distinct role in behavioral science research: Data describe which empirical patterns were observed and theory explains why empirical patterns were observed or are expected to be observed.

Also: Theories do not explain data - theories explain phenomena!

3. Lists of Variables or Constructs Are Not Theory

Papers […] often are written as if well-defined variables or constructs, by themselves, are enough to make theory. Sometimes the list of variables represents a logical attempt to cover all or most of the determinants of a given outcome or process.

Such lists may be useful catalogs of variables that can be entered as predictors or controls in multiple regression equations […], but they do not constitute theory.

Lists of constructs (without an explicit definition of their causal relationship) often are called a framework.

4. Diagrams (Alone) Are Not Theory

Diagrams or figures can be a valuable part of a research paper but also, by themselves, rarely constitute theory. Probably the least theoretical representations are ones that simply list categories of variables such as “personality,” “environmental determinants,” or “demographics.”

More helpful are figures that show causal relationships in a logical ordering, so that readers can see a chain of causation or how a third variable intervenes in or moderates a relationship. Also useful are temporal diagrams showing how a particular process unfolds over time. […]

As Whetten (1989) suggested, while boxes and arrows can add order to a conception by explicitly delineating patterns and causal connections, they rarely explain why the proposed connections will be observed. Some verbal explication is almost always necessary.

5. Hypotheses (or Predictions) Are Not Theory.

Hypotheses can be an important part of a well-crafted conceptual argument. They serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized. […]

Hypotheses do not (and should not) contain logical arguments about why empirical relationships are expected to occur. Hypotheses are concise statements about what is expected to occur, not why it is expected to occur.

Phenomena: Stable and general features of the world in need of explanation. Can be understood as robust generalizations of patterns in empirical data. They are the explanatory targets for scientific theories (the explanandum). In psychology often called “effects”.

Data: Relatively direct observations. Refer to particular empirical patterns in concrete data sets rather than empirical generalizations (which would be phenomenona).

Explanatory Theories are constructed to explain the empirical phenomena that are evidenced by data (i.e., they are the explanans). Explanatory theories can be expressed in terms of a set of linked propositions referring to latent psychological constructs. At least one of these propositions expresses a general principle.

“theory T putatively explains phenomenon P” means “if the world were as T says it is, P would follow as a matter of course.” Although this notion of explanation is arguably incomplete, it has the advantages of being close to the commonsense understanding of the concept and being easy to implement in a formal model—namely by creating a virtual world in which theory T is true and showing that this world will indeed produce phenomenon P.

• Phänomen: Eine Eigenschaft der Welt, die hinreichend wenig flüchtig ist, so dass wir diese wahrnehmen und beschreiben können (Explanandum einer Theorie)
• Begriff / Konzept: Semantische Einheit (Konzept), die die Merkmale eines Gegenstandes oder Sachverhaltes vereint. ≠ Wort/Wortmarke
• Relationship
• Modell: Vereinfachte Abbildung eines Ausschnitts der Wirklichkeit. Alle Strukturen, die die Sätze einer Theorie erfüllen, sind Modelle dieser Theorie. Oder andersherum ausgedrückt: Eine Theorie kann meist in verschiedenen Modellen implementiert werden, die alle kompatibel zur Theorie sind.
• Operationalisierung: Präzise Angabe (inkl. Begründung), wie ein Begriff (s.o.), zu messen ist.
• Daten: Daten sind öffentliche Aufzeichnungen, die durch Messungen und Experimente erzeugt werden und als Beweis für die Existenz oder Merkmale von Phänomenen dienen. (Woodward, 2009). Daten enthalten das Phänomen, an dem wir interessiert sind, aber auch Rauschen (Messfehler, Verzerrung durch den Experimentator, Transkriptionsfehler). Deshalb erklären Theorien die Phänomene, nicht die Daten. (Fried, 2021)